New capabilities include AI Catalog and the next-generation of automated feature engineering.

DataRobot, the enterprise AI company, unveiled new features to its Enterprise AI platform designed to automate the entire end-to-end data science process, introducing an AI Catalog and next-generation automated feature engineering.

In the race to innovate with AI, organizations must embrace a solution that can automate the time-intensive process for everything associated with AI success, including identifying relevant data sources, preparing data for machine learning, building and deploying machine learning models, and monitoring and managing models over time.

In its latest release, DataRobot has added an AI Catalog to its software. This is based on DataRobot’s February acquisition of Cursor, a data collaboration platform founded to help organizations find, understand, and use data more efficiently.

The AI Catalog creates a collaborative environment for enterprise AI by providing users with the ability to search for any dataset, share new sources, and comment and tag assets to promote understanding and reuse. AI Catalog also assists with data science productivity by providing the ability to prepare and manage feature lists to share and use in new projects.

Additional data management benefits include the ability to connect to any location to access data -- whether it’s in a data lake, database, in the cloud, or on-premise.

Automated Feature Engineering

DataRobot pioneered automated feature engineering and makes extensive use of it in its Automated Machine Learning and Automated Time Series products. The release of DataRobot’s new AI Catalog has enabled the next-generation of automated feature engineering by allowing users to automatically discover new features from multiple related datasets.

Manual feature engineering is often considered the most laborious and time-consuming step in the data science workflow. By automating this process, DataRobot is greatly accelerating how users prepare datasets to improve machine learning model performance.

These new capabilities, which are the culmination of research and development DataRobot has conducted over the last three years, enable users to quickly find new data from multiple sources and apply simple business rules to automate the creation of large numbers of useful features and the subsequent transformation of those features for each specific algorithm. This market leading capability allows users to build better machine learning models in less time and increase the pace of innovation with AI.

Machine Learning Operations (MLOps)

This latest release also includes DataRobot MLOps, a new solution for deploying, monitoring, and managing machine learning models across the enterprise that was announced earlier this month. With DataRobot MLOps, DataRobot customers now have a single dashboard to deploy models and see the status of all production models independent of where they were created or where they are deployed.

“This release fulfills the shared vision we have with our customers to allow them to control the end-to-end data science process with a single platform,” said Phil Gurbacki, senior vice president of product and customer experience at DataRobot. “This is one of the most significant platform updates we have ever made and we’re confident that these new features will propel our customers to succeed in the AI-driven era. This release will provide companies with an advanced product suite to automate every aspect of AI and derive true, lifetime value from their data.”

In addition to these highlights, DataRobot also added a number of frequently requested features. These include times series accuracy enhancements, a word cloud for multi-class models, a new residuals analysis, a DataRobot add-in for Excel, API support for feature fit, and much more. For more information on these significant new features.

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